| 1. |
- Anderson, Rachele, et al.
(författare)
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Classification of EEG signals based on mean-square error optimal time-frequency features
- 2018
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Ingår i: 2018 26th European Signal Processing Conference, EUSIPCO 2018. - 9789082797015 ; 2018-September, s. 106-110
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Konferensbidrag (refereegranskat)abstract
- This paper illustrates the improvement in accuracy of classification for electroencephalogram (EEG) signals measured during a memory encoding task, by using features based on a mean square error (MSE) optimal time-frequency estimator. The EEG signals are modelled as Locally Stationary Processes, based on the modulation in time of an ordinary stationary covariance function. After estimating the model parameters, we compute the MSE optimal kernel for the estimation of the Wigner-Ville spectrum. We present a simulation study to evaluate the performance of the derived optimal spectral estimator, compared to the single windowed Hanning spectrogram and the Welch spectrogram. Further, the estimation procedure is applied to the measured EEG and the time-frequency features extracted from the spectral estimates are used to feed a neural network classifier. Consistent improvement in classification accuracy is obtained by using the features from the proposed estimator, compared to the use of existing methods.
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| 2. |
- Anderson, Rachele, et al.
(författare)
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Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
- 2019
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Ingår i: Applied Sciences (Switzerland). - : MDPI AG. - 2076-3417 .- 1454-5101. ; 9:23
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose a novel framework for the analysis of task-related heart rate variability (HRV). Respiration and HRV are measured from 92 test participants while performing a chirp-breathing task consisting of breathing at a slowly increasing frequency under metronome guidance. A non-stationary stochastic model, belonging to the class of Locally Stationary Chirp Processes, is used to model the task-related HRV data, and its parameters are estimated with a novel inference method. The corresponding optimal mean-square error (MSE) time-frequency spectrum is derived and evaluated both with the individually estimated model parameters and the common process parameters. The results from the optimal spectrum are compared to the standard spectrogram with different window lengths and the Wigner-Ville spectrum, showing that the MSE optimal spectral estimator may be preferable to the other spectral estimates because of its optimal bias and variance properties. The estimated model parameters are considered as response variables in a regression analysis involving several physiological factors describing the test participants’ state of health, finding a correlation with gender, age, stress, and fitness. The proposed novel approach consisting of measuring HRV during a chirp-breathing task, a corresponding time-varying stochastic model, inference method, and optimal spectral estimator gives a complete framework for the study of task-related HRV in relation to factors describing both mental and physical health and may highlight otherwise overlooked correlations. This approach may be applied in general for the analysis of non-stationary data and especially in the case of task-related HRV, and it may be useful to search for physiological factors that determine individual differences.
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| 3. |
- Axmon, Joakim, et al.
(författare)
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Partial Forward-Backward Averaging for Enhanced Frequency Estimation of Real X-texture Modes
- 2005
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Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 53:7, s. 2550-2562
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, enhancement of the signal root estimation of a particular kind of real-valued two-dimensional (2-D) sinusoidal modes is considered. To its constitution, each mode corresponds to the superposition of two real-valued plane waves in a particular symmetry. The concept of partial forward-backward averaging, which is applicable for modes that are undamped in at least one dimension, is introduced as a means for improving the signal subspace estimate from which the signal roots are estimated. The consequences of real-valued signals for the signal root estimates are discussed in detail, and it is shown that by applying partial forward-backward averaging, the mean square errors of the estimates, and the breakdown threshold signal-to-noise ratio (SNR), are significantly reduced, compared with forward-only or conventional forward-backward (when applicable) usage of the sampled signals. The practical implication is highlighted by applying the proposed technique to modal analysis of multichannel impact responses from a tree trunk.
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| 4. |
- Brynolfsson, Johan, et al.
(författare)
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A time-frequency-shift invariant parameter estimator for oscillating transient functions using the matched window reassignment
- 2021
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684. ; 183
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we present the matched window reassignment method, generalizing the results to complex valued signals in multiple dimensions. For an oscillating transient signal with an envelope shape described by an arbitrary twice differentiable function, the reassigned spectrogram, with a matched window, concentrates all energy into one single time-frequency point. An estimator for the parameters of the envelope, in multiple dimensions, is constructed using the above property where the concentration is measured using the Rényi entropy. Furthermore, we present a classification scheme, where an observation is classified based on the concentration when reassigning with a set of model functions. Finally, two examples of parameter estimation from real-world measurements are shown, a one-dimensional time series of a single dolphin click and a two-dimensional time-series of seismic data.
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| 5. |
- Brynolfsson, Johan, et al.
(författare)
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Least Squares and Maximum Likelihood Estimation of Mixed Spectra
- 2018
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Ingår i: 26th European Signal Processing Conference, EUSIPCO 2018.. - 9789082797015 ; , s. 2345-2349
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a novel 1-D spectralestimator for signals with mixed spectra. The proposed methodis partly based on the recently introduced smooth spectralestimator LIMES, in which the smoothness is accounted for byassuming linearity within predefined segments of the spectrum.The proposed method utilizes this formulation but also allowssegments to change size to better estimate the spectrum, therebyallowing for the estimation of spectra that are neither completelysmooth or sparse in frequency, but rather contains a mixtureof such components. Using simulated data, we illustrate theperformance of the proposed estimator, comparing to other recentspectral estimation techniques.
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| 6. |
- Brynolfsson, Johan, et al.
(författare)
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Multitaper Estimation of the Coherence Spectrum in low SNR
- 2014
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Ingår i: European Signal Processing Conference. - 2219-5491.
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Konferensbidrag (refereegranskat)abstract
- A pseudo coherence estimate using multitapers is presented. The estimate has better localization for sinusoids and is shown to have lower variance for disturbances compared to the usual coherence estimator. This makes it superior in terms of finding coherent frequencies between two sinusoidal signals; even when observed in low SNR. Different sets of multitapers are investigated and the weights of the final coherence estimate are adjusted for a low-biased estimate of a single sinusoid. The proposed method is more computationally efficient than data dependent methods, and does still give comparable results.
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| 7. |
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| 8. |
- Brynolfsson, Johan, et al.
(författare)
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Optimal Time-Frequency analysis of the multiple time-translated locally stationary processes
- 2013
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Ingår i: [Host publication title missing].
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Konferensbidrag (refereegranskat)abstract
- A previously proposed model for non-stationary signals is extended in this contribution. The model consists of mul- tiple time-translated locally stationary processes. The opti- mal Ambiguity kernel for the process in mean-square-error sense is computed analytically and is used to estimate the time-frequency distribution. The performance of the kernel is compared with other commonly used kernels. Finally the model is applied to electrical signals from the brain (EEG) measured during a concentration task.
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| 9. |
- Brynolfsson, Johan, et al.
(författare)
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Parameter estimation of Oscillating Gaussian functions using the scaled reassigned spectrogram
- 2018
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Ingår i: Signal Processing. - : Elsevier BV. - 0165-1684. ; 150, s. 20-32
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we suggest an algorithm for estimation of the parameters detailing Oscillating Gaussian functions. The different components of the signal are first detected in the spectrogram. After this we exploit the fact that a Gaussian function may be perfectly reassigned into one single point given a correct scaling factor, where this scaling factor is a function of the unknown shape parameter of the Gaussian function. The scaled reassignment of the spectrogram is performed using a set of candidate scaling factors and the local Renyi entropy is used to measure the concentration of each component using every candidate scaling factor. The estimates are refined by using non-linear least squares. The algorithm is evaluated on both simulated and real data.
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| 10. |
- Brynolfsson, Johan, et al.
(författare)
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Smooth Time-Frequency Estimation using Covariance Fitting
- 2014
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Ingår i: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. - 1520-6149. ; , s. 779-783
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Konferensbidrag (refereegranskat)abstract
- In this paper, we introduce a time-frequency spectral estimator for smooth spectra, allowing for irregularly sampled measurements. A non-parametric representation of the time dependent (TD) covariance matrix is formed by assuming that the spectrum is piecewise linear. Using this representation, the time-frequency spectrum is then estimated by solving a convex covariance fitting problem, which also, as a byproduct, provides an enhanced estimation of the TD covariance matrix. Numerical examples using simulated non-stationary processes show the preferable performance of the proposed method as compared to the classical Wigner-Ville distribution and a smoothed spectrogram.
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